医学
泊松回归
混淆
肺癌
人口
比例危险模型
相对风险
外科
环境卫生
内科学
置信区间
作者
Caitlin M. Milder,Michael Bellamy,Sara C. Howard,Elizabeth D. Ellis,Ashley P. Golden,Sarah S. Cohen,Michael T. Mumma,Benjamin French,Lydia B. Zablotska,John D. Boice
出处
期刊:Occupational and Environmental Medicine
[BMJ]
日期:2024-08-15
卷期号:81 (9): 439-447
被引量:2
标识
DOI:10.1136/oemed-2023-109192
摘要
Objective This follow-up study of uranium processing workers at the Fernald Feed Materials Production Center examines the relationship between radiation exposure and cancer and non-cancer mortality among 6403 workers employed for at least 30 days between 1951 and 1985. Methods We estimated cumulative, individual, annualised doses to 15 organs/tissues from external, internal and radon exposures. Vital status and cause of death were ascertained in 2017. The analysis employed standardised mortality ratios, Cox proportional hazards and Poisson regression models. Competing risk analysis was conducted for cardiovascular disease (CVD) mortality risk given several assumptions about risk independent of competing outcomes. Emphysema was examined to assess the potential for confounding by smoking. Results Vital status was confirmed for 98.1% of workers, with 65.1% deceased. All-cause mortality was less than expected in salaried but not hourly workers when compared with the US population. A statistically significant dose response was observed between external (but not total or internal) lung dose and lung cancer mortality (HR at 100 mGy adjusted for internal dose=1.45; 95% CI=1.05 to 2.01). Significantly increased HRs at 100 mGy dose to heart were observed for CVD (1.27; 95% CI=1.07 to 1.50) and ischaemic heart disease (1.30; 95% CI=1.07 to 1.58). CVD risk remained elevated regardless of competing risk assumptions. Both external and internal radiation were associated with emphysema. Conclusions Lung cancer was associated with external dose, though positive dose responses for emphysema imply residual confounding by smoking. Novel use of competing risk analysis for CVD demonstrates leveraging retrospective data for future risk prediction.
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